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plot_img_spec.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
from procedures import *
from astropy.io import fits
#import matplotlib # To avoid crashing when ssh into Narit using putty
#if not 'DISPLAY' in os.environ:
# matplotlib.use('agg') # To avoid crashing when ssh into Narit using putty, however this means plots are not shown (test when working in front of uhppc30)
import matplotlib.pyplot as plt
import matplotlib.cm as cmaps
import matplotlib.colors as colors
import sys
from astropy.table import Table
"""try:
from gatspy.periodic import LombScargleFast
except:
print('Warn: gatspy could not be loaded. Did you activate the Anaconda hiflex environment?')
LombScargleFast = None"""
import tkcanvas as tkc
if sys.version_info[0] < 3:
import Tkinter as Tk
from collections import OrderedDict as dict
else:
import tkinter as Tk
import copy
import pickle
def plot_image(image, spaths, pctile=0, show=False, adjust=[0.05,0.95,0.95,0.05], title='', return_frame=False, frame=None, autotranspose=True, colorbar=True, axis_name=['x [px]','y [px]','flux [ADU]'], size=32):
"""
Plots the raw CCD image to files $spaths$
:param image: numpy 2D array, containing the CCD image
:param spath: path to save the plot to
:param pctile: Percentile of the picture to use in the plot. 0 means min and max values are used
:param show: if True then plot is shown instead of saved
:param adjust: to avoid big white areas in the figure
:param title: Titel for the plot
:return:
"""
ims = image.shape
im_good = image[~np.isnan(image)]
if frame is None:
fig, frame = plt.subplots(1, 1)
if type(size).__name__ not in ('list', 'ndarray'):
size = [size]
fig.set_size_inches(size[0], size[-1])
if len(ims) == 2:
if ims[0] > ims[1] and show == True and autotranspose == True: #transpose the image
image = np.transpose(image)
title = title + ' - transposed'
plt.subplots_adjust(left=adjust[0], right=adjust[1], top=adjust[2], bottom=adjust[3])
if type(pctile).__name__ == 'list':
vmin = pctile[0]
vmax = pctile[1]
else:
vmin = np.percentile(im_good,pctile)
vmax = np.percentile(im_good,100-pctile)
cframe = frame.imshow(image, cmap='gray',vmin=vmin, vmax=vmax)
frame.set_xlabel(axis_name[0], fontsize=14)
frame.set_ylabel(axis_name[1], fontsize=14)
frame.set_title(title, fontsize=16)
if colorbar:
cbar = plt.colorbar(cframe,fraction=0.024, pad=0.02)
cbar.set_label(axis_name[2], fontsize=14)
cbar.ax.invert_yaxis()
if return_frame:
return frame
if show:
print("\n Please close the graph (Figure 1) to continue (Alt F4).\n")
plt.show()
else:
if type(spaths).__name__ not in ('list', 'ndarray'):
spaths = [spaths]
for spath in spaths:
plt.savefig(spath, bbox_inches='tight')
plt.close()
def plot_spectra(spectra_x, spectra_y, labels, spaths, show=False, adjust=[0.05,0.95,0.95,0.05, 1.0,1.01], title='', return_frame=False, frame=None, size=[16.2, 10]):
"""
Plots the Spectra to files $spaths$
:param spectra: ???
:param spath: path to save the plot to
:param show: if True then plot is shown instead of saved
:param adjust: to avoid big white areas in the figure: left, right, top, bottom, legend left, legend top
:param title: Titel for the plot
:return:
"""
#spectra_x = np.array(spectra_x)
if frame is None:
fig, frame = plt.subplots(1, 1)
if type(size).__name__ not in ('list', 'ndarray'):
size = [size]
fig.set_size_inches(size[0], size[-1])
plt.subplots_adjust(left=adjust[0], right=adjust[1], top=adjust[2], bottom=adjust[3])
dx = (np.max(spectra_x) - np.min(spectra_x))*0.01
dy = (np.max(spectra_y) - np.min(spectra_y))*0.01
plt.axis([np.min(spectra_x)-dx,np.max(spectra_x)+dx, np.min(spectra_y)-dy,np.max(spectra_y)+dy])
#frame.plot(spectra_x,spectra_y)#, label=labels)
if len(spectra_x.shape) == 1:
frame.plot(spectra_x, spectra_y, label=labels)
else:
for i in range(len(spectra_x)):
frame.plot(spectra_x[i], spectra_y[i], label=labels[i])
frame.set_xlabel('x [px]', fontsize=14)
frame.set_ylabel('flux [ADU]', fontsize=14)
frame.set_title(title, fontsize=16)
frame.legend(loc='upper left', bbox_to_anchor=(adjust[4], adjust[5]))
if return_frame:
return frame
elif show:
print("\n Please close the graph (Figure 1) to continue (Alt F4).\n")
plt.show()
else:
if type(spaths).__name__ not in ('list', 'ndarray'):
spaths = [spaths]
for spath in spaths:
plt.savefig(spath, bbox_inches='tight')
plt.close()
def create_plot_linestyle_marker_markersize_color(length, linestyle=None, marker=None, markersize=None, color=None):
"""
:param length: integer, number of individual plots in the graph
:param linestyle, marker, color: List, Array, String, or None: use the given properties for the plot. String will be transformed to a list, and the list will multiplied by the number of entries
If None is given for color then the matplotlib standards are used
:return cycler_plt: cycler.Cycler, contains the information to make a easy looking graph
"""
length = max(1,length)
cycler_ori = plt.rcParams["axes.prop_cycle"] # Get the current properties of the cycler
#if linestyle == None: # replace None by empty string
# linestyle = ['']
#if marker == None: # replace None by empty string
# marker = ['']
if type(linestyle).__name__ == 'str': # Make string to list
linestyle = [linestyle]
if type(marker).__name__ == 'str': # Make string to list
marker = [marker]
if type(color).__name__ == 'str': # Make string to list
color = [color]
if type(markersize).__name__ == 'int' or type(markersize).__name__ == 'float': # Make number to list
markersize = [markersize]
if linestyle != None:
for i in range(len(linestyle)): # replace None by empty string
if linestyle[i] == None:
linestyle[i] = ''
if marker != None:
for i in range(len(marker)): # replace None by empty string
if marker[i] == None:
marker[i] = ''
if color != None: # user color
cycler_plt = plt.cycler(color=color*length)
else: # No color given
if 'color' in cycler_ori.by_key(): # user original colors
cycler_plt = plt.cycler(color=cycler_ori.by_key()['color']*length)
else: # use dummy colors
cycler_plt = plt.cycler(color=[u'#1f77b4', u'#ff7f0e', u'#2ca02c', u'#d62728', u'#9467bd', u'#8c564b', u'#e377c2', u'#7f7f7f', u'#bcbd22', u'#17becf']*length)
if type(marker) == list or type(marker) == np.ndarray:
cycler_plt += plt.cycler(marker=marker*length)
elif 'marker' in cycler_ori.by_key():
cycler_plt += plt.cycler(marker=cycler_ori.by_key()['marker']*length)
if type(linestyle) == list or type(linestyle) == np.ndarray:
cycler_plt += plt.cycler(linestyle=linestyle*length)
elif 'linestyle' in cycler_ori.by_key():
cycler_plt += plt.cycler(linestyle=cycler_ori.by_key()['linestyle']*length)
if type(markersize) == list or type(markersize) == np.ndarray:
cycler_plt += plt.cycler(markersize=markersize*length)
return cycler_plt
def plot_points(data_x, data_y, labels, spaths, show=False, adjust=[0.05,0.95,0.95,0.05, 1.0,1.01], title='', return_frame=False, frame=None, x_title='x', y_title='y', linestyle="", marker="o", markersize=None, color=None, size=[16.2, 10]):
"""
Plots the Spectra to files $spaths$
:param spectra: ???
:param spath: path to save the plot to
:param show: if True then plot is shown instead of saved
:param adjust: to avoid big white areas in the figure: left, right, top, bottom, legend left, legend top
:param title: Titel for the plot
:param size: size of the plot. For display on screen: 16.2, 10 ; for publication (single column): 6.7, 4.1; for publication, 2 columns: 3.3, 2.1
:return:
"""
if frame is None:
fig, frame = plt.subplots(1, 1)
if type(size).__name__ not in ('list', 'ndarray'):
size = [size]
fig.set_size_inches(size[0], size[-1])
plt.subplots_adjust(left=adjust[0], right=adjust[1], top=adjust[2], bottom=adjust[3])
minx, miny, maxx, maxy = 1e10, 1e10, -1e10, -1e10
for i in range(len(data_x)):
if type(data_x[i]) in [np.ndarray, list]:
if sum(~np.isnan(list(data_x[i]))) > 0:
minx = min(minx, np.nanmin(data_x[i]) )
maxx = max(maxx, np.nanmax(data_x[i]) )
data_yi = np.array(data_y[i], dtype=float) # Necessary, because data_y[i][ ~np.isnan(data_y[i]) ] fails, float necessary, otherwise data_yi.dtype will object, which fails np.isnan
#print type(data_yi), data_yi.shape, data_yi.dtype, np.isnan(data_yi)
if sum(~np.isnan(data_yi)) > 0: # only search min/max, if non-nan data exists
miny = min(miny, np.nanmin(data_yi) )
maxy = max(maxy, np.nanmax(data_yi) )
else:
minx = min(minx, data_x[i] )
maxx = max(maxx, data_x[i] )
miny = min(miny, data_y[i] )
maxy = max(maxy, data_y[i] )
#miny,maxy = -0.3,0.3
#minx,maxx = 0,4250
dx = max(1,maxx - minx)*0.01
dy = max(1,maxy - miny)*0.01
plt.axis([minx-dx,maxx+dx, miny-dy,maxy+dy])
#print(len(data_x), linestyle, marker, markersize, color
cycler_plt = create_plot_linestyle_marker_markersize_color(len(data_x), linestyle, marker, markersize, color)
frame.set_prop_cycle(cycler_plt)
label = ''
plot_legend = False
for i in range(len(data_x)):
if len(labels) > 0:
if len(labels) > i:
label = labels[i]
else:
print('Warn: No label for dataset {0}'.format(i))
label = None
if label == '':
label = None
if label is not None:
plot_legend = True
if type(data_x[i]) is not np.ndarray and type(data_x[i]) is not list:
frame.plot(data_x[i], data_y[i], label=label)#, linestyle=linestyle[i], marker=marker[i])
elif len(data_x[i]) - len(data_y[i]) == 1: # Histogram (one data point less in y
width = (( 1 - i/len(data_x) ) * 0.7 + 0.1) * (data_x[i][1] - data_x[i][0])
center = (data_x[i][:-1] + data_x[i][1:]) / 2
plt.bar(center, data_y[i], align='center', width=width, label=label)
elif len(data_x[i]) > 0:
frame.plot(data_x[i], data_y[i], label=label)#, linestyle=linestyle[i], marker=marker[i])
frame.set_xlabel(x_title, fontsize=13)
frame.set_ylabel(y_title, fontsize=13)
frame.set_title(title, fontsize=15)
if plot_legend:
frame.legend(loc='upper left', bbox_to_anchor=(adjust[4], adjust[5]))
if return_frame:
return frame
elif show:
print("\n Please close the graph (Figure 1) to continue (Alt F4).\n")
plt.show()
else:
if type(spaths).__name__ not in ('list', 'ndarray'):
spaths = [spaths]
for spath in spaths:
plt.savefig(spath, bbox_inches='tight')
plt.close()
def plot_spectra_UI(im, title=''):
ims = im.shape
iml = len(ims)
settings = dict()
if os.path.isfile('plot_settings.pkl') == True:
settings = load_obj('plot_settings')
fig, frame = plt.subplots(1, 1, figsize=(8,6))
#plt.subplots_adjust(left=adjust[0], right=adjust[1], top=adjust[2], bottom=adjust[3])
def plot(**param):
frame, im = param['frame'], param['im']
x_range, y_range, old_xlabel_text = copy.copy(frame.get_xlim()), copy.copy(frame.get_ylim()), copy.copy(frame.get_xlabel())
frame.clear()
size_inch = copy.deepcopy(fig.get_size_inches())
adjust=[0.04,0.97,0.97,0.03, 1.0,1.01]
adjust[0] = max(0.04, 0.2 - 0.04682*(size_inch[0] - 3.8)** 0.4416) # left
adjust[3] = max(0.03, 0.0 + 0.47866*(size_inch[1] - 0.0)**-0.9368) # bottom
if param['draw_filenames']:
frame.set_title(title, fontsize=14)
adjust[2] = 0.75
if param['draw_legend']:
adjust[1] = min(0.95, 0.7 + 0.07518*(size_inch[0] - 3.8)** 0.4289) # right
plt.subplots_adjust(left=adjust[0], right=adjust[1], top=adjust[2], bottom=adjust[3])
param['name_data_-1'] = ['data']
param['data'] = im
for j in range(0,iml-1):
param['name_data_{0}'.format(j)]=[]
for data_entry in param['name_data_{0}'.format(j-1)]:
data = param[data_entry]
for i in range(ims[j]):
if ( param['exclude_{0}'.format(j)] and i not in param['plot_sub_{0}'.format(j)] ) or (not param['exclude_{0}'.format(j)] and i in param['plot_sub_{0}'.format(j)]):
if data_entry+'_{0}'.format(i) not in param.keys(): # Only load if necessary
param[data_entry+'_{0}'.format(i)] = data[i]
param['name_data_{0}'.format(j)].append(data_entry+'_{0}'.format(i))
#print 'param.keys()',param.keys()
#print "param['name_data_{0}'.format(iml-1-1)]",param['name_data_{0}'.format(iml-1-1)]
settings = dict()
for entry in param.keys():
if entry.find('plot_sub_') == 0 or entry.find('exclude_') == 0 or entry in ['wavelength_bool', 'plot_format', 'reset_plotx', 'reset_ploty', 'draw_legend', 'draw_filenames', 'xlabel', 'ylabel']:
settings[entry] = param[entry]
save_obj(settings, 'plot_settings')
plot_ranges = [1E10,0,1E10,0]
xlabel_text = 'Nothing to plot -> modify parameters or remove plot_settings.pkl'
if 'name_data_{0}'.format(iml-1-1) in param.keys():
for data_entry in tqdm(param['name_data_{0}'.format(iml-1-1)]):
data_label = data_entry.replace('data_','')
data = np.array(param[data_entry], float)
if param['wavelength_bool']: # Use wavelength
w_pos = 0
#searchpos = param['plot_format'].find('w')+1
searchpos = 1
for searchlength in range(len(param['plot_format'])-searchpos, 0,-1):
try:
w_pos = int(param['plot_format'][searchpos:searchpos+searchlength+1])
break
except:
w_pos = 0
data_entry_w = data_entry.rsplit('_',iml-2) # iml-2 should be 2 for nomal images
if iml == 4: # for normal images
#data_entry_w = data_entry_w[0]+'_0_'+data_entry_w[2] # Select the wavelength in the data file
data_entry_w = '{0}_{1}_{2}'.format(data_entry_w[0],w_pos,data_entry_w[2]) # Select the wavelength in the data file
elif iml ==3: # for images with linearised wavelength
#data_entry_w = data_entry_w[0]+'_0' # barycentric
data_entry_w = '{0}_{1}'.format(data_entry_w[0],w_pos) # uncorrected wavelength
else:
print('not implemented')
if data_entry_w not in param.keys():
text = data_entry_w.split('_')
if iml == 4:
param[data_entry_w] = im[int(text[1]), int(text[2]), int(text[3]) ]
elif iml ==3:
param[data_entry_w] = im[int(text[1]), int(text[2]) ]
x_axis = np.array(param[data_entry_w], float)
xlabel_text = 'wavelength [Angstrom] (dataset {0})'.format(w_pos)
else:
x_axis = np.arange(len(data), dtype=float)
xlabel_text = 'x [px]'
x_axis = x_axis[~np.isnan(data)]
data = data[~np.isnan(data)]
""" Create a test signal
A = 27. # amplitude
period = 31 # in x_axis uits
omega = 2. * np.pi / period # radians per second
phi = 0.5 * np.pi # radians
# Create the full signal
data = A * np.sin(omega * x_axis + phi)"""
if param['plot_format'] == 'f': # make a fft
x_axis = np.fft.fftfreq(len(data))
data = np.abs(np.fft.fft(data)) # px -> 1/px (dT -> f = 1/dT)
good_values = list(range(1,int(len(data)/2))) # 0 contains the sum of the data, and n/2+1 the negative frequencies
data = data * 2 / data.shape[0]
data, x_axis = data[good_values], 1/x_axis[good_values]
xlabel_text = 'period [px]'
elif param['plot_format'].find('l') != -1: # http://joseph-long.com/writing/recovering-signals-from-unevenly-sampled-data/
nout = min(20000, 4*len(data)) # number of frequency-space points at which to calculate the signal strength (output) # 10k needed 4 min to calculate for 35k datapoints
# the posible Periods in px scale: 2 to 10% length of the data; in wave-scale: diff between 2 closest to 10% diff between furthest away:
period_range = np.array([ 2*np.nanmin(np.abs(x_axis[1:] - x_axis[:-1])), 0.5*(np.nanmax(x_axis)-np.nanmin(x_axis)) ])
freq_range = 1.0 / period_range
"""model = LombScargleFast().fit(x_axis, data-np.nanmedian(data))
df = (max(freq_range) - min(freq_range)) / nout
freqs = min(freq_range) + df * np.arange(nout)
periods = 1/freqs
power = model.score_frequency_grid(min(freq_range), df, nout)
#periods, power = model.periodogram_auto(nyquist_factor=0.5)
data = power"""
freqs = np.linspace(min(freq_range), max(freq_range), nout)
periods = 1.0 / freqs
angular_freqs = 2 * np.pi * freqs
#print len(x_axis), len(data), angular_freqs, data.dtype, x_axis.dtype
pgram = scipy.signal.lombscargle(x_axis, data, angular_freqs)
normalized_pgram = np.sqrt(4 * (pgram / data.shape[0]))
data = normalized_pgram
x_axis = periods
if param['wavelength_bool']:
xlabel_text = 'period [Angstrom]'
else:
xlabel_text = 'period [px]'
if len(x_axis) > 0:
if param['draw_legend']:
frame.plot(x_axis, data, label=data_label)
else:
frame.plot(x_axis, data)
#print sum(data[70:3000]-min(data[70:3000])), sum(data[1550:3000]-min(data[1550:3000]))
plot_ranges[0] = min(plot_ranges[0], min(x_axis))
plot_ranges[1] = max(plot_ranges[1], max(x_axis))
plot_ranges[2] = min(plot_ranges[2], min(data))
plot_ranges[3] = max(plot_ranges[3], max(data))
if param['reset_ploty'] or y_range == (0.0, 1.0):
dy = (plot_ranges[3] - plot_ranges[2])*0.01
y_range = (plot_ranges[2]-dy, plot_ranges[3]+dy)
param['reset_ploty'] = True
if old_xlabel_text != xlabel_text or param['reset_plotx'] or x_range == (0.0, 1.0):
dx = (plot_ranges[1] - plot_ranges[0])*0.01
x_range = (plot_ranges[0]-dx,plot_ranges[1]+dx)
param['reset_plotx'] = True
plt.axis([x_range[0], x_range[1], y_range[0], y_range[1]])
ylabel_text = 'flux [ADU]'
if len(param['xlabel']) > 0:
xlabel_text = param['xlabel']
if len(param['ylabel']) > 0:
ylabel_text = param['ylabel']
frame.set_xlabel(xlabel_text, fontsize=14)
frame.set_ylabel(ylabel_text, fontsize=14)
if param['draw_legend']:
frame.legend(loc='upper left', bbox_to_anchor=(adjust[4], adjust[5]))
# get kwargs
pkwargs = dict()
pkwargs['frame'] = frame
pkwargs['im'] = im
# define valid_function
# input is one variable (the string input)
# return is either:
# True and values
# or
# False and error message
def vfunc(xs):
try:
xwhole = xs.replace(',', ' ')
xwhole = xwhole.replace(': ', ':')
new_xs = xwhole.split()
xs = []
for entry in new_xs:
if entry.find(':') > 0:
entry = entry.split(':')
for j in range(int(entry[0]),int(entry[1])+1):
xs.append(j)
else:
xs.append(int(entry))
return True, xs
except:
return False, ('Error, input must consist of integers \n '
'separated by commas or white spaces')
def vfunc_bool(xs):
if xs.lower() in ['true', 'yes']:
xs = True
else:
xs = False
return True, xs
# define widgets
widgets = dict()
pkwargs['wavelength_bool'] = settings.get('wavelength_bool', False)
widgets['wavelength_bool'] = dict(label='Wavelength\ninstead of\nPixel',
kind='CheckBox', start=pkwargs['wavelength_bool'])
pkwargs['plot_format'] = settings.get('plot_format', '')
widgets['plot_format'] = dict(label='Wavelength set,\nFFT, or\nLomb-Scargle',
comment='number/f/l' ,
kind='TextEntry', minval=None, maxval=None,
fmt=str, start=pkwargs['plot_format'], valid_function=None,
width=5)
for i in range(iml-1):
starta, startb = [], True
text = [ 'Which data?', 'comma or colon\nseparated list' ]
if (iml-1) - i - 1 == 2:
text = [ 'Which file', 'comma or colon\nseparated list']
if (iml-1) - i - 1 == 1:
text = [ 'Which data', 'list']
starta, startb = [1], False
if (iml-1) - i - 1 == 0:
text = [ 'Which aperture', 'list']
starta, startb = [int(ims[-2]/2)], False
if 'plot_sub_{0}'.format(i) in settings.keys():
starta = settings['plot_sub_{0}'.format(i)]
if 'exclude_{0}'.format(i) in settings.keys():
startb = settings['exclude_{0}'.format(i)]
startat = str(starta).replace('[','').replace(']','') # make the list into a text
widgets['plot_sub_{0}'.format(i)] = dict(label=text[0], comment=None, #text[1],
kind='TextEntry', minval=None, maxval=None,
fmt=str, start=startat, valid_function=vfunc,
width=12)
widgets['exclude_{0}'.format(i)] = dict(label='Exclude',
kind='CheckBox', start=startb)
pkwargs['plot_sub_{0}'.format(i)] = starta
pkwargs['exclude_{0}'.format(i)] = startb
pkwargs['reset_plotx'] = settings.get('reset_plotx', False)
widgets['reset_plotx'] = dict(label='reset plot x',
kind='CheckBox', start=pkwargs['reset_plotx'])
pkwargs['reset_ploty'] = settings.get('reset_ploty', False)
widgets['reset_ploty'] = dict(label='reset plot y',
kind='CheckBox', start=pkwargs['reset_ploty'])
pkwargs['draw_legend'] = settings.get('draw_legend', True)
widgets['draw_legend'] = dict(label='draw legend',
kind='CheckBox', start=pkwargs['draw_legend'])
pkwargs['draw_filenames'] = settings.get('draw_filenames', True)
widgets['draw_filenames'] = dict(label='draw filenames',
kind='CheckBox', start=pkwargs['draw_filenames'])
#widgets['draw_size'] = dict(label='12.8 x 9.6 inch',
# kind='CheckBox', start=False)
#pkwargs['draw_size'] = False
pkwargs['xlabel'] = settings.get('xlabel', '')
pkwargs['ylabel'] = settings.get('ylabel', '')
widgets['xlabel'] = dict(label='x label',
kind='TextEntry', minval=None, maxval=None,
fmt=str, start=pkwargs['xlabel'], valid_function=None,
width=14)
widgets['ylabel'] = dict(label='y label',
kind='TextEntry', minval=None, maxval=None,
fmt=str, start=pkwargs['ylabel'], valid_function=None,
width=14)
widgets['accept'] = dict(label='Close', kind='ExitButton', position=Tk.BOTTOM)
widgets['update'] = dict(label='Update', kind='UpdatePlot', position=Tk.BOTTOM)
# run initial update plot function
plot(**pkwargs)
wprops = dict(orientation='v', position=Tk.RIGHT)
gui3 = tkc.TkCanvas(figure=fig, ax=frame, func=plot, kwargs=pkwargs,
title='Plot spectral orders', widgets=widgets,
widgetprops=wprops)
gui3.master.mainloop()
# Start of code
if __name__ == "__main__":
#read the data
rpath = '/data/ronny/20170630_harps_test_data_red/'
fitsfiles=[]
#fitsfiles.append(['MasterBias_HARPS.fits',0,'i']) #one CCD
#fitsfiles.append(['MasterBias_HARPS.fits',1,'i']) #other CCD
#fitsfiles.append(['FlatOb_HARPS.fits',0,'i']) #object fiber
#fitsfiles.append(['FlatOb_HARPS.fits',1,'i'])
#fitsfiles.append(['FlatCo_HARPS.fits',0,'i']) #other fiber
#fitsfiles.append(['FlatCo_HARPS.fits',1,'i'])
#fitsfiles.append(['Flat_HARPS.fits',0,'i']) #both fibers
#fitsfiles.append(['Flat_HARPS.fits',1,'i'])
#fitsfiles.append(['BACR_FLAT_HARPS.fits',0,'i']) #???
#fitsfiles.append(['BACB_FLAT_HARPS.fits',0,'i'])
#fitsfiles.append(['P_ob_B_HARPS.fits',0,'i']) #traces?
#fitsfiles.append(['P_ob_R_HARPS.fits',0,'i'])
#fitsfiles.append(['B_flat_ob_HARPS.fits',1,'s']) #object fiber, B chip, flux
#fitsfiles.append(['B_flat_ob_HARPS.fits',2,'s']) #object fiber, B chip, error?
#fitsfiles.append(['R_flat_ob_HARPS.fits',1,'s']) #
#fitsfiles.append(['R_flat_ob_HARPS.fits',2,'s']) #
#fitsfiles.append(['BACR_FLAT_CO_HARPS.fits',0,'i']) #
#fitsfiles.append(['BACB_FLAT_CO_HARPS.fits',0,'i']) #
#fitsfiles.append(['P_co_B_HARPS.fits',0,'i']) #traces?
#fitsfiles.append(['P_co_R_HARPS.fits',0,'i']) #
#fitsfiles.append(['B_flat_co_HARPS.fits',1,'s']) #
#fitsfiles.append(['B_flat_co_HARPS.fits',2,'s']) #
#fitsfiles.append(['R_flat_co_HARPS.fits',1,'s']) #
#fitsfiles.append(['R_flat_co_HARPS.fits',2,'s']) #
#fitsfiles.append(['BACR_HARPS.2014-04-01T19:30:52.924.fits',0,'i']) #
#fitsfiles.append(['BACB_HARPS.2014-04-01T19:30:52.924.fits',0,'i']) #arc lines and something
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.ob.B.fits.S',1,'s']) #emission
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.ob.B.fits.S',2,'s'])
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.ob.R.fits.S',1,'s']) #
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.ob.R.fits.S',2,'s'])
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.co.B.fits.S',1,'s']) #emission
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.co.B.fits.S',2,'s'])
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.co.R.fits.S',1,'s']) #
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.spec.co.R.fits.S',2,'s'])
rpath = '/data/ronny/20170630_harps_test_data/'
#fitsfiles.append(['HARPS.2014-04-01T19:30:52.924.fits',0,'i']) #B, both fibers: arcs
#rpath = '/data/ronny/Reduced/20170706_test/extracted/'
#fitsfiles.append(['master_flat_Order_13.fits',0,'s']) #
#rpath = 'extracted/'
#for i in range(1,37):
# fitsfiles.append(['SunArc-{0}_10s.npy'.format('%4.4i'%i),0,'sg']) #20170912
#fitsfiles.append(['Arc_long.npy',0,'sg']) #20170913, 20170912
#fitsfiles.append(['Arc_short.npy',0,'sg']) #20170913
#fitsfiles.append(['FlatArc-0001_10s.npy',0,'sg']) #20170913
#fitsfiles.append(['FlatArc-0001_4s.npy',0,'sg']) #20170922
#for i in range(1,10):
# fitsfiles.append(['Fiberbend-{0}_4s.npy'.format('%4.4i'%i),0,'sg']) #20170922
if len(sys.argv) > 1:
filenames = []
for filename in sys.argv[1:]:
if filename.find('plot_') != -1:
filenames.append(filename)
else:
filenames = ['plot_files.lst']
for filename in filenames:
rpath = ''
files = read_text_file(filename, no_empty_lines=True, warn_missing_file=True)
for line in files:
if line[0] != '#':
fitsfiles.append([line.replace(' ',''),0,'sg'])
print('Found {0} files in {1}.'.format(len(fitsfiles), filename))
data_sg = []
titel_sg = ''
for fitsfile in fitsfiles:
fname = rpath+fitsfile[0]
if not os.path.isfile(fname):
print('Warn: File {0} does not exist'.format(fname))
continue
if fitsfile[0].find('.npy')>0:
im=np.load(fname)
else:
im = np.array(fits.getdata(fname,0))
ims = im.shape
#print ims
if fitsfile[2] == 'i': # For images
if len(ims) == 3: # select the right image, if more than one is in the data
im = im[:,:,fitsfile[1]]
plot_image(im, ['savepaths'], 1, True, [0.05,0.95,0.95,0.05], str(fitsfile))
elif fitsfile[2] == 's':
if len(ims) == 3:
imx = []
imy = []
labels = []
for i in range(ims[0]):
imx.append(im[i,0,:])
imy.append(im[i,fitsfile[1],:])
labels.append('Order ' + str(i+1))
#imx = np.transpose(np.array(imx))
#imy = np.transpose(np.array(imy))
elif len(ims) == 1:
ims = Table.read(fname)
imx = np.array(ims['pixel number'])
imy = np.array(ims['counts'])
imx = imx[~np.isnan(imy)]
imy = imy[~np.isnan(imy)]
#print(imx.shape,imy.shape,imx, imy)
labels = ''
else:
imx = im[0,:]
imy = im[fitsfile[1],:]
labels = ''
plot_spectra(imx,imy,labels, ['savepaths'], True, [0.06,0.92,0.95,0.06, 1.0,1.01], str(fitsfile))
elif fitsfile[2] == 'sg':
ims = np.insert(ims, 0, 1) # Reshape to make the insert easier
im.shape = ims
if len(data_sg) == 0:
data_sg = im
else:
datas = data_sg.shape
while len(ims) < len(datas): # Different number of data-subsets
im = im[..., np.newaxis].reshape(np.insert(ims, 2, 1)) # add a new axis, first at the end, and then move to second place by reshaping
ims = im.shape
while len(datas) < len(ims): # Different number of data-subsets
data_sg = data_sg[..., np.newaxis].reshape(np.insert(datas, 2, 1)) # add a new axis, first at the end, and then move to second place by reshaping
print('Added a new dimension at the third place')
datas = data_sg.shape
#print 2,datas, ims
for i in range(1, len(ims)): # Diffent sizes of the array
if ims[i] < datas[i]:
ims = list(ims)
ims[i] = datas[i] - ims[i]
temp = np.empty(ims)
temp.fill(np.nan)
im = np.append(im, temp, axis=i)
elif datas[i] < ims[i]:
datas = list(datas)
datas[i] = ims[i] - datas[i]
temp = np.empty(datas)
temp.fill(np.nan)
data_sg = np.append(data_sg, temp, axis=i)
ims = im.shape
datas = data_sg.shape
data_sg = np.append(data_sg, im, axis=0)
#print data_sg.shape
titel_sg += ',\n{0}'.format(fitsfile[0])
#print data_sg.shape, data_sg.dtype, data_sg.itemsize, data_sg.nbytes, sys.getsizeof(data_sg), data_sg.nbytes # size about the size of the fits file
if len(data_sg) > 0:
max_orders = 0
for im in data_sg:
max_orders = max(max_orders, im.shape[1])
for i in range(len(data_sg)):
nr_orders = data_sg[i].shape[1]
if nr_orders < max_orders:
data_sg[i] = np.insert(data_sg[i], list(range(nr_orders,max_orders)), np.zeros(data_sg[i].shape[2]), axis=1)
print('Data read ({0} MB), plotting data now'.format(round(data_sg.nbytes/1024./1024,1)) )
plot_spectra_UI(np.array(data_sg), title=titel_sg[2:])
#no of orders, 3 different values, px